Papers Self-Supervised Learning
“Self-Supervised Learning” 태그가 달린 논문 5,929편 · 필터 해제
Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models
Voice anonymization aims to protect speaker identity while preserving linguistic content and speech usability. However, most anonymization systems are developed on adult speech, leading to degraded performance when appli…
Self-Supervised LearningDomain AdaptationBEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations
Self-supervised learning enables audio representations that transfer across domains and tasks. We present BEST-RQ-2, an evolution of BEST-RQ that retains frozen randomprojection-based discrete targets while introducing a…
Self-Supervised LearningSpatially Localized Image Degradation Embeddings for Image Quality Assessment
Self-supervised learning (SSL) currently drives state-of-the-art performance in no-reference image quality assessment (NR-IQA). However, standard SSL pipelines uniformly apply synthetic distortions across the entire imag…
No-Reference Image Quality AssessmentSelf-Supervised LearningA Self-Supervised Learning Framework for Video Encoding Complexity Clustering
Adaptive video streaming is a widely used technique for delivering video content over the internet. One of the key challenges is determining the optimal encoding settings for each video, which can vary significantly base…
Self-Supervised LearningContrastive LearningThe Platonic Defense: Backdoor Defense for Self-Supervised Encoders in the Era of Large Scale Pre-training
Self-supervised learning (SSL) pretrained models have become a dominant paradigm for visual representation learning, but they are vulnerable to backdoor attacks. Existing defenses struggle to defend against such attacks …
Self-Supervised LearningRepresentation LearningFrom Phase to Phenomenon: Self-Supervised Learning of Subsurface Scattering with Minimal Phase-shift Inputs
We propose a self-supervised pretraining framework for learning sub-surface scattering (SSS) light transport representations from minimal input. Our method leverages a stereo projector-camera setup that captures only eig…
Self-Supervised LearningTowards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks
Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure. Logging of all cloud computing events enables investigation into any…
Self-Supervised LearningAnomaly DetectionSciFlow: Semantic Cross Interference for Self-Supervised Optical Flow Domain Generalization
Motions of objects and scenes carry essential intelligence in video understanding, offering rich cues for interpreting dynamic settings and interactions. Due to the cost and scarcity of high-quality annotation or ground …
Self-Supervised LearningDomain GeneralizationOctoSense: Self-Supervised Learning for Multimodal Robot Perception
We present OctoSense, an open-source sensor platform with stereo RGB and event cameras, LiDAR, a thermal camera, an inertial measurement unit, RTK-corrected global positioning system, and proprioception (CAN bus data fro…
Self-Supervised LearningSemantic SegmentationTacVerse: A Multi-Sensor Dataset and Benchmark for Cross-Sensor Vision-Based Tactile Perception
Vision-based tactile sensors (VBTSs) enable robots to infer contact geometry and force-related cues by imaging deformation through an internal camera, yet generalisation across sensor designs remains poorly understood. W…
Self-Supervised LearningA welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks
The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential f…
Self-Supervised LearningContrastive LearningImage AugmentationFew-Shot LearningMJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning
Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in video data, extending this success to joint…
Self-Supervised LearningRepresentation LearningEvaluating self-supervised echocardiographic representations across downstream extraction strategies for left-ventricular segmentation and ejection fraction estimation
Self-supervised learning (SSL) is increasingly used in medical imaging to reduce annotation requirements, but representation quality is often judged using a single downstream evaluation setting. For dense clinical tasks,…
Self-Supervised LearningHedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark
We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m$^2$ spatial resolution. We combine and harmonize multiple remote sensi…
Self-Supervised Learning3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy
Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells. We present a systematic comparison of 2D and 3D masked autoencoders (MAE-2D vs…
Self-Supervised LearningRepresentation LearningProtein Language ModelFetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection
Automated frame selection for fetal biometry remains under addressed, with most prior work targeting generic quality assessment or downstream measurement pipelines that assume suitable frames are available. We introduce …
Self-Supervised LearningLearning by Shifting: Temporal View Construction for Time Series Contrastive Learning
Supervised learning demands large quantities of labeled data, a bottleneck that is expensive and reliant on domain-specific expertise. Self-supervised learning, particularly contrastive learning, has emerged as a compell…
Time Series ClassificationSelf-Supervised LearningRepresentation LearningContrastive LearningSPOTR: Spatio-temporal Pooling One-Token Reconstruction for Universal Physiological Signal Self-supervised Learning
Physiological signals such as EEG, ECG, and PPG are widely used in clinical monitoring. Recent self-supervised learning (SSL) methods offer an attractive way to leverage unlabeled recordings, yet they still fall short in…
Self-Supervised LearningHow Well Do Self-Supervised Speech Models Encode Age and Gender in Children's Speech? A Layer-Wise Analysis Across Multiple Architectures
Self-supervised learning (SSL) models have become a central component of modern speech processing systems, as they enable the learning of rich acoustic representations without reliance on labeled data. Despite their succ…
Age And Gender ClassificationSelf-Supervised LearningMultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learning
Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns. However, it also causes harmful retention of noise and outliers, degrading generaliza…
Self-Supervised LearningContrastive Learning